{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e74a2fd3",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/nyandwi/machine_learning_complete/blob/main/6_classical_machine_learning_with_scikit-learn/11_a_practical_intro_to_principal_components_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f35cb9f9",
   "metadata": {},
   "source": [
    "*This notebook was created by [Jean de Dieu Nyandwi](https://twitter.com/jeande_d) for the love of machine learning community. For any feedback, errors or suggestion, he can be reached on email (johnjw7084 at gmail dot com), [Twitter](https://twitter.com/jeande_d), or [LinkedIn](https://linkedin.com/in/nyandwi).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5ba4fe",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "#  Practical Intro to Principal Components Analysis for Dimension Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e2c5a6",
   "metadata": {},
   "source": [
    "## Intro to PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef43c5a",
   "metadata": {},
   "source": [
    "PCA standing for Principal Component Analysis is mostly used for reducing the dimension of datasets, and for such reason it's commonly known as dimensional reduction algorithm. \n",
    "\n",
    "Because of PCA's dimensional reduction ability, it can be used:\n",
    "\n",
    "* To visualize high dimensional datasets, particuraly because visualizing such high level datasets is impractical. \n",
    "\n",
    "* Before a typical machine learning model training to merely increase the training speed, given that the training data is reduced or no longer contain redundant features. It is not guaranteed to speed up but in some cases, it can. \n",
    "\n",
    "* Select most useful features while getting the rid of useless information. But not always, sometime useful information will be lost too especially if the original data was already good and didn't contain noises. \n",
    "\n",
    "While the above can sound compelling (and they are), the features returned by PCA can be hard to interpret and it adds complexity that could turn out to be disadvantages than advantages.  \n",
    "\n",
    "PCA is well implemented in [Scikit-Learn](https://scikit-learn.org/stable/modules/decomposition.html#latent-dirichlet-allocation-lda) and it has different versions such as Randomized PCA, Kernel PCA, and Incremental PCA(mostly for online learning). \n",
    "\n",
    "In many ML resources, you will find PCA in the category of unsupervised learning algorithms. Here is a simple reason: PCA reduces the dimension of datasets without instructions (labels in other word) of how that is going to be done other than specifying the number of principal components, just like specifying the number of clusters in KMeans clustering. \n",
    "\n",
    "While the maths and deep concepts behind PCA are beyond the scope this notebook, we will try to have a practical understanding of the above PCA's usecases. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5e8372",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* [1 - Imports](#1)\n",
    "\n",
    "* [2 - PCA for Dimensional Reduction](#2)\n",
    "\n",
    "    * [2.1 Loading the Data](#2-1)\n",
    "    * [2.2 Applying PCA](#2-2)\n",
    "    * [2.3 Visualizing the Principal Components](#2-3)\n",
    "    * [2.4 Interpreting the Principal Components](#2-4)\n",
    "    * [2.5 Explained Variance Ratio](#2-5)\n",
    "    * [2.6 Choosing the Right Number of Components](#2-6)\n",
    "    \n",
    "    \n",
    "* [3 - PCA for Digits Visualization](#3)\n",
    "    * [3.1 Loading the Data](#3-1)\n",
    "    * [3.2 Using PCA to reduce the Digits Dimensionality](#3-2)\n",
    "    * [3.3 Visualizing the Digits](#3-3)\n",
    "\n",
    "* [4- Final Notes](#4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99058d12",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df278c5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35699c38",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "\n",
    "## 2 - PCA for Dimension Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fa4492",
   "metadata": {},
   "source": [
    "In this section, we are going to use PCA for reducing the dimension of a structured dataset. In the next example, we will use it for visualizing high dimensional datasets. \n",
    "\n",
    "We will use wine dataset. Check the data description down. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82722c2c",
   "metadata": {},
   "source": [
    "<a name='2-1'></a>\n",
    "\n",
    "### 2.1 Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a6562b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "wine = load_wine()\n",
    "\n",
    "# Features\n",
    "X = wine.data \n",
    "\n",
    "# Target feature\n",
    "y = wine.target\n",
    "\n",
    "# Features names\n",
    "feat_names = wine.feature_names\n",
    "\n",
    "# Target name\n",
    "target_names = wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa3cf04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run below line for dataset description\n",
    "\n",
    "#wine.DESCR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6020bde",
   "metadata": {},
   "source": [
    "Now that we have loaded the data, let's inspect it a little bit to see what kind of the data we have. We can try to see the general information about it since the mere goal here is to reduce the dimension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f97b7345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0aada191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0127031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24b1d31f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "545821ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d129da49",
   "metadata": {},
   "source": [
    "The dataset has 13 features. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d343bc0",
   "metadata": {},
   "source": [
    "Before applying PCA to the dataset, let's first scale the input data with `StandardScaler`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49e9f5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263b74a3",
   "metadata": {},
   "source": [
    "<a name='2-2'></a>\n",
    "\n",
    "### 2.2 Applying PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bde0e38",
   "metadata": {},
   "source": [
    "Scikit-Learn is remarkably well designed. Just like how simple it is to use almost all algorithms implemented in Scikit-Learn, using PCA is no different. It's just `fit` and `transform`. \n",
    "\n",
    "In the process to reduce the dataset dimensionality, we have to specify the number of principal components. Think principal components as coordinates that we want to project the data in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8bc6c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7440459a",
   "metadata": {},
   "source": [
    "**Important notes**: Using the scaled data (`X_scaled`) in `pca.fit_transform()` reduces the explained variance ratio to about [`0.36198848, 0.1920749`]. If you have found any mismatch in the section of `2.5 Explained Variance Ratio`, this was the reason. \n",
    "\n",
    "I will use the unscaled version of the data for such reason. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "814662b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_reduced = pca.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fea7e098",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_reduced = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "681e65f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of orginal dataset(without PCA):(178, 13)\n",
      "The shape of reduced dataset due to PCA:(178, 2)\n"
     ]
    }
   ],
   "source": [
    "# Shape of the reduced data\n",
    "\n",
    "print('The shape of orginal dataset(without PCA):{}\\nThe shape of reduced dataset due to PCA:{}'.format(X.shape, X_reduced.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f53375",
   "metadata": {},
   "source": [
    "That was so fast. For now, we have reduced the dataset from 13 dimensions to 2 dimensions. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b29590b",
   "metadata": {},
   "source": [
    "<a name='2-3'></a>\n",
    "\n",
    "### 2.3 Visualizing the Principal Components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af33e229",
   "metadata": {},
   "source": [
    "Let's try to visualize the 2 principal components. We will also add the labels of the wine datasets or target_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "844cc576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f90300bcdd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wine_colors are assumptions\n",
    "\n",
    "wine_colors = ['orangered', 'darkred', 'crimson']\n",
    "num_classes = [0,1,2]\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "for color, num_class, target_name in zip(wine_colors, num_classes, target_names):\n",
    "    \n",
    "    plt.scatter(X_reduced[y==num_class,0], X_reduced[y==num_class,1], \n",
    "                c=color,linewidths=8,label=target_name, cmap='jet')\n",
    "    \n",
    "plt.title('PCA of Red Wine Dataset')\n",
    "plt.xlabel('First Component')\n",
    "plt.ylabel('Second Component')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa9f5ec",
   "metadata": {},
   "source": [
    "For the scatter plot, above we plotted the two principal components and also used colors to differentiate the classes. \n",
    "\n",
    "Although we are able to visualize the entire dataset (by reducing the dimension) and we can see all 3 classes in one plot due to that it is reduced into 2 components, it's hard to interpret these components. \n",
    "\n",
    "Let's see the components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4eed54",
   "metadata": {},
   "source": [
    "<a name='2-4'></a>\n",
    "\n",
    "### 2.4 Interpreting the Principal Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9f03006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.65926472e-03, -6.81015556e-04,  1.94905742e-04,\n",
       "        -4.67130058e-03,  1.78680075e-02,  9.89829680e-04,\n",
       "         1.56728830e-03, -1.23086662e-04,  6.00607792e-04,\n",
       "         2.32714319e-03,  1.71380037e-04,  7.04931645e-04,\n",
       "         9.99822937e-01],\n",
       "       [ 1.20340617e-03,  2.15498184e-03,  4.59369254e-03,\n",
       "         2.64503930e-02,  9.99344186e-01,  8.77962152e-04,\n",
       "        -5.18507284e-05, -1.35447892e-03,  5.00440040e-03,\n",
       "         1.51003530e-02, -7.62673115e-04, -3.49536431e-03,\n",
       "        -1.77738095e-02]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "63115909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of the components \n",
    "\n",
    "pca.components_.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3de53a",
   "metadata": {},
   "source": [
    "The first row of the above components is the First Principal Component and the second row is the Second Principal Component. The columns are the features. \n",
    "\n",
    "In order to understand these components, we can also visualize them using heatmap. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73dc974a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "sns.heatmap(pca.components_, cmap='viridis',\n",
    "            xticklabels=feat_names, \n",
    "            yticklabels=['First Component', 'Second Component'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51114bc7",
   "metadata": {},
   "source": [
    "The above heatmap can be easy to interpret. It shows how much of a given feature is represented in the particular component. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0038a6b9",
   "metadata": {},
   "source": [
    "<a name='2-5'></a>\n",
    "\n",
    "### 2.5 Explained Variance Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c876f94",
   "metadata": {},
   "source": [
    "Explained Variance Ratio shows the percentage of the dataset variance explained by each principal components.\n",
    "\n",
    "`pca.explained_variance_` will show the whole variance amount. Whereas `pca.explained_variance_ratio_` will show the percentage variance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ccd0486f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99201.78951748,   172.53526648])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3c89689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99809123, 0.00173592])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790b5cc9",
   "metadata": {},
   "source": [
    "The Explained Variance Ration in our case is `[0.99809123, 0.00173592]`. It means that 99.8% of the dataset variance lies on the first component, and the rest 0.17% lies on the second component.\n",
    "\n",
    "If you look back to the heatmap above, on the y axis specifically, the above can make sense. \n",
    "\n",
    "**Important notes**: If you got the explained variance ratio of `[0.36198848, 0.1920749 ]`, the reason was that I used the scaled data and probably didn't rerun the cell above. Using the unscaled data in `pca.fit_transform()` should give the explained variance ratio of (or close to) `[0.99809123, 0.00173592]` ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a2310d",
   "metadata": {},
   "source": [
    "<a name='2-6'></a>\n",
    "\n",
    "### 2.6 Choosing Number of Components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6916615",
   "metadata": {},
   "source": [
    "One of the hardest thing using PCA might be to choose the number of principal components that will retain as much as information as possible. Most of the time, 2 or 3 components will be it, but you want to choose them based off the variance ratio. \n",
    "\n",
    "There is a better way to find them using `cumsum`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e33993d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the right number of components by plotting no of components Vs explained variance ratio\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca_2 = PCA()\n",
    "\n",
    "pca_2.fit(X_scaled)\n",
    "\n",
    "# Explained variance ratio\n",
    "exp_var_ratio = pca_2.explained_variance_ratio_\n",
    "\n",
    "# Cumulative sum of the variance ratios\n",
    "\n",
    "cumsum = np.cumsum(exp_var_ratio)\n",
    "\n",
    "# Plotting number of components Vs explained variance ratio\n",
    "\n",
    "plt.plot(cumsum)\n",
    "\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3d35a0",
   "metadata": {},
   "source": [
    "Another way to find them is to instead of passing the number of components in PCA object, pass the variance ration that you would like instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d44f05bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the number of components from desired explained variance ratio\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca_3 = PCA(n_components=0.96)\n",
    "\n",
    "X_reduced = pca_3.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e787137",
   "metadata": {},
   "source": [
    "Let's now find the number of components using `pca_3.n_components_`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a402c2aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_3.n_components_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb16ef44",
   "metadata": {},
   "source": [
    "And here is the explained variance ratio. It should be very close to `0.96`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3b260f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9616971684450644"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pca_3.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9993f5be",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "## 3. PCA for Visualizing Hand Written Digits\n",
    "\n",
    "Like we have been saying, PCA can be used to visualize the high dimensional dataset. \n",
    "\n",
    "Let us attempt to visualize the handwritten digits, a classical dataset you may already know if you're reading this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b955b77c",
   "metadata": {},
   "source": [
    "<a name='3-1'></a>\n",
    "### 3.1 Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6466ac1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a60c09e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits_data = digits.data\n",
    "\n",
    "digits_data .shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80c4ccb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the digits to see the differences when scaled and when not scaled \n",
    "\n",
    "# digits_scaled = scaler.fit_transform(digits_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec07c50",
   "metadata": {},
   "source": [
    "Each digits has 64 dimensions, that is 8*8 pixels. We can use PCA to project those 64 dimensions into 2 dimensions or components. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6fd36a",
   "metadata": {},
   "source": [
    "<a name='3-2'></a>\n",
    "### 3.2 Using PCA to Reduce the Dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b0466da",
   "metadata": {},
   "outputs": [],
   "source": [
    "dig_pca = PCA(n_components=2)\n",
    "\n",
    "digit_reduced = dig_pca.fit_transform(digits_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec1d9b9",
   "metadata": {},
   "source": [
    "Let's see the shape of the reduced digits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "490d50d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of orginal digits(without PCA):(1797, 64)\n",
      "The shape of reduced digits due to PCA:(1797, 2)\n"
     ]
    }
   ],
   "source": [
    "# Shape of the reduced digits\n",
    "\n",
    "print('The shape of orginal digits(without PCA):{}\\nThe shape of reduced digits due to PCA:{}'.format(digits_data.shape, digit_reduced.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498d5941",
   "metadata": {},
   "source": [
    "<a name='3-3'></a>\n",
    "### 3.3 Visualizing the Digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bdbc3f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f906990fe50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.scatter(digit_reduced[:,0],digit_reduced[:,1],\n",
    "           c=digits.target,cmap=('jet'), edgecolor='none')\n",
    "\n",
    "plt.xlabel('First Principal Component')\n",
    "plt.ylabel('Second Principal Component')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1848d2",
   "metadata": {},
   "source": [
    "That is so fantastic. Imagine that we are able to visualize all 10 digits into one plot, just because we have reduced their dimensions from 64 to 2. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31680101",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "\n",
    "## 4. Final Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f3bf7d",
   "metadata": {},
   "source": [
    "This notebook was all about PCA. The main application of PCA is to reduce the dimension of the datasets, which in turn can motivate visualizations of reduced dataset. \n",
    "\n",
    "The version of the PCA used is Normal PCA but as we mentioned in the beginning, there are other version of PCA such as Randomized PCA, Kernel PCA and Incremental PCA. If you would like to learn more about those PCAs, check out the Scikit-Learn documentation of the section of [dimension reduction](https://scikit-learn.org/stable/modules/decomposition.html#decompositions). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4335acd6",
   "metadata": {},
   "source": [
    "\n",
    "## [BACK TO TOP](#0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe4af0c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit ('tensor': conda)",
   "language": "python",
   "name": "python3710jvsc74a57bd034ac5db714c5906ee087fcf6e2d00ee4febf096586592b6ba3662ed3b7e7a5f6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
